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[General] New Google Generative-AI-Leader Test Online | Test Generative-AI-Leader Sample Q

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【General】 New Google Generative-AI-Leader Test Online | Test Generative-AI-Leader Sample Q

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Google Generative-AI-Leader Exam Syllabus Topics:
TopicDetails
Topic 1
  • Business Strategies for a Successful Generative AI Solution: This section of the exam measures the skills of Cloud Architects and evaluates the ability to design, implement, and manage enterprise-level generative AI solutions. It covers the decision-making process for selecting the right solution, integrating AI into an organization, and measuring business impact. A strong emphasis is placed on secure AI practices, highlighting Google’s Secure AI Framework and cloud security tools, as well as the importance of responsible AI, including fairness, transparency, privacy, and accountability.
Topic 2
  • Fundamentals of Generative AI: This section of the exam measures the skills of AI Engineers and focuses on the foundational concepts of generative AI. It covers the basics of artificial intelligence, natural language processing, machine learning approaches, and the role of foundation models. Candidates are expected to understand the machine learning lifecycle, data quality, and the use of structured and unstructured data. The section also evaluates knowledge of business use cases such as text, image, code, and video generation, along with the ability to identify when and how to select the right model for specific organizational needs.
Topic 3
  • Google Cloud’s Generative AI Offerings: This section of the exam measures the skills of Cloud Architects and highlights Google Cloud’s strengths in generative AI. It emphasizes Google’s AI-first approach, enterprise-ready platform, and open ecosystem. Candidates will learn about Google’s AI infrastructure, including TPUs, GPUs, and data centers, and how the platform provides secure, scalable, and privacy-conscious solutions. The section also explores prebuilt AI tools such as Gemini, Workspace integrations, and Agentspace, while demonstrating how these offerings enhance customer experience and empower developers to build with Vertex AI, RAG capabilities, and agent tooling.
Topic 4
  • Techniques to Improve Generative AI Model Output: This section of the exam measures the skills of AI Engineers and focuses on improving model reliability and performance. It introduces best practices to address common foundation model limitations such as bias, hallucinations, and data dependency, using methods like retrieval-augmented generation, prompt engineering, and human-in-the-loop systems. Candidates are also tested on different prompting techniques, grounding approaches, and the ability to configure model settings such as temperature and token count to optimize results.

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Google Cloud Certified - Generative AI Leader Exam Sample Questions (Q36-Q41):NEW QUESTION # 36
A financial services company receives a high volume of loan applications daily submitted as scanned documents and PDFs with varying layouts. The manual process of extracting key information is time- consuming and prone to errors. This causes delays in loan processing and impacts customer satisfaction. The company wants to automate the extraction of this critical data to improve efficiency and accuracy. Which Google Cloud tool should they use?
  • A. Document AI API
  • B. Dataflow
  • C. Natural Language API
  • D. Vision AI
Answer: A
Explanation:
Document AI API is specifically designed for intelligent document processing. It uses machine learning to extract structured data from unstructured documents like scanned forms and PDFs, even with varying layouts.
This directly addresses the challenge of automating data extraction from loan applications. Natural Language API focuses on text understanding, Vision AI on image analysis (not structured extraction from documents), and Dataflow is for data processing pipelines.
________________________________________

NEW QUESTION # 37
A development team is configuring a generative AI model for a customer-facing application and wants to ensure the generated content is appropriate and harmless. What is the primary function of the safety settings parameter in a generative AI model?
  • A. To filter out potentially harmful or inappropriate content from the model's output based on the desired level of filtering.
  • B. To determine the number of tokens the model can process at once by influencing the complexity and length of inputs and outputs.
  • C. To limit the maximum text length that the model generates by ensuring concise responses.
  • D. To control the creativity and randomness of the model's output by adjusting the diversity of word choices.
Answer: A
Explanation:
Safety settings in generative AI models are specifically designed to prevent the generation of content that could be harmful, offensive, or inappropriate. This includes filtering for categories like hate speech, sexually explicit content, self-harm, and violence, based on predefined thresholds. Options A, B, and D refer to other parameters like max_output_tokens or temperature, which control output length, input/output processing, and creativity, respectively, not safety.
________________________________________

NEW QUESTION # 38
An organization wants to use generative AI to create a chatbot that can answer customer questions about their account balances. They need to ensure that the chatbot can access previous portions of the conversation with the customer. Which prompting technique should they use?
  • A. Use few-shot prompting.
  • B. Use role prompting.
  • C. Use prompt chaining.
  • D. Use zero-shot prompting.
Answer: C
Explanation:
Prompt chaining (or conversational memory/context management) is the technique used to maintain the conversational context. It involves feeding previous turns of a conversation (or a summary of them) back into the model along with the current user query, allowing the chatbot to "remember" and reference past interactions for coherent and contextually relevant responses, especially crucial for tasks like checking account balances that span multiple turns.
________________________________________

NEW QUESTION # 39
A finance team wants to use Gemma to help with daily tasks so that the financial analysts can focus on other work. Which business problem can Gemma most efficiently address?
  • A. The challenge of efficiently producing high-quality written summaries and initial drafts of financial communications.
  • B. The difficulty in analyzing large datasets of financial transactions and market data to identify anomalies and predict future financial performance.
  • C. The struggle to accurately extract key financial figures and insights from a variety of document formats, such as balance sheets and income statements, for quick reporting.
  • D. The complexity of building and deploying sophisticated internal knowledge bases to answer employees' finance-related questions with accurate and up-to-date information.
Answer: A
Explanation:
Gemma is a family of lightweight, open-source Large Language Models (LLMs) from Google that are based on the same research and technology as the Gemini models. As an LLM, its core strength lies in language-based tasks, particularly the generation and summarization of text.
The problem that Gemma, or any pure LLM, can most efficiently address is:
Generating text: creating new content quickly (Option D).
Summarizing text: condensing long communications or documents (Option D).
Option D, producing high-quality written summaries and initial drafts, is a natural language generation task that aligns perfectly with the core function of an LLM like Gemma. It is a key productivity booster for analysts needing to draft reports or emails quickly.
Option B (Analyzing large datasets/predicting performance) requires traditional machine learning (ML) models or analytical tools like BigQuery ML, as LLMs are not specialized for numerical predictive modeling.
Option C (Extracting key financial figures from documents) is a task for a highly specialized tool like Google's Document AI.
Option A (Building internal knowledge bases for Q&A) is a broader use case that is best solved with a platform solution using RAG, such as Vertex AI Search, not just a base model.
(Reference: Google's description of the Gemma model family emphasizes its role as a flexible, open LLM that excels at language fundamentals, making it ideal for content creation, summarization, and other text generation tasks.)

NEW QUESTION # 40
What is a primary benefit of using a multi-agent system?
  • A. To serve as a platform for hosting traditional, non-AI applications.
  • B. To simplify the most basic and repetitive rule-based tasks.
  • C. To manage complex tasks that demand coordinated AI functions.
  • D. To consolidate all unique AI functions into a single, undifferentiated model.
Answer: C
Explanation:
Multi-agent systems are designed to tackle complex problems by breaking them down into sub-tasks, where each agent specializes in a specific function. These agents then coordinate and collaborate to achieve a larger, more intricate goal that a single, monolithic AI model might struggle with.
________________________________________

NEW QUESTION # 41
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